class_docstr: Mu-law encoding (compression) of audio signal, in [-1, 1], to [0, quantization_channels - 1].
  See `Wikipedia <https://en.wikipedia.org/wiki/Μ-law_algorithm>`_ for more details.
  
  Args:
      quantization_channels (positive int): Number of channels. For 8-bit encoding, use 256.
      **kwargs: optional keyword args for `tf.keras.layers.Layer()`
  
  Note:
      Mu-law encoding was originally developed to increase signal-to-noise ratio of signal during transmission.
      In deep learning, mu-law became popular by `WaveNet <https://arxiv.org/abs/1609.03499>`_ where
      8-bit (256 channels) mu-law quantization was applied to the signal so that the generation of waveform amplitudes
      became a single-label 256-class classification problem.
  
  Example:
      ::
  
          input_shape = (2048, 1)  # mono signal (float in [-1, 1])
          model = Sequential()
          model.add(kapre.MuLawEncoding(quantization_channels=256, input_shape=input_shape))
          # now the shape is (batch, time=2048, ch=1) with int in [0, quantization_channels - 1]
code: def __init__(
      self,
      quantization_channels,
      **kwargs,
  ):
      super(MuLawEncoding, self).__init__(**kwargs)
      self.quantization_channels = quantization_channels
  
imports: import tensorflow
  from tensorflow.keras.layers import Layer
  from tensorflow.keras import backend
  from .None import backend
  from .backend import _CH_FIRST_STR, _CH_LAST_STR, _CH_DEFAULT_STR
label_desc: Use image node_id 1 to create a new MuLawEncoding object from inherited base classes: Layer with example: obj = MuLawEncoding(quantization_channels)